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      Transcriptome analysis of the mouse fetal and adult rete ovarii and surrounding tissues

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          Abstract

          The rete ovarii (RO) is an epithelial structure that arises during fetal development in close proximity to the ovary and persists throughout adulthood in mice. However, the functional significance of the RO remains elusive, and it has been absent from recent discussions of female reproductive anatomy. The RO comprises three distinct regions: the intraovarian rete (IOR) within the ovary, the extraovarian rete (EOR) in the periovarian tissue, and the connecting rete (CR) linking the EOR and IOR. We hypothesize that the RO plays a pivotal role in maintaining ovarian homeostasis and responding to physiological changes. To uncover the nature and function of RO cells, we conducted transcriptome analysis, encompassing bulk, single-cell, and nucleus-level sequencing of both fetal and adult RO tissues using the Pax8-rtTA; Tre-H2B-GFP mouse line, where all RO regions express nuclear GFP. This study presents three datasets, which highlight RO-specific gene expression signatures and reveal differences in gene expression across the three RO regions during development and in adulthood. The integration and rigorous validation of these datasets will advance our understanding of the RO’s roles in ovarian development, female maturation, and adult female fertility.

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          This study employs comprehensive bulk, single cell and single nucleus transcriptome analysis to uncover gene expression signatures of the fetal and adult rete ovarii (RO).

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          Most cited references29

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                06 November 2023
                : 2023.11.06.565717
                Affiliations
                [1 ]Department of Cell Biology, Duke University Medical Center, Durham NC 27710
                [2 ]Section of Developmental Biology, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora CO 80045
                Author notes

                Author contributions

                DNA, YX, DTP and JM carried out the experiments; DNA, RO, and JM carried out the data processing and data analysis, RO formatted and deposited the data in appropriate repositories and designed the shiny app to query the data; DNA, BC and JM designed the experiments; DNA, BC and JM drafted, revised and edited the manuscript; BC and JM acquired funding for the project.

                [* ]Corresponding author: jennifer.mckey@ 123456cuanschutz.edu
                Author information
                http://orcid.org/0000-0003-0435-4749
                http://orcid.org/0000-0003-1198-6963
                http://orcid.org/0000-0001-8333-0782
                http://orcid.org/0000-0003-3334-6166
                http://orcid.org/0000-0002-6587-0969
                http://orcid.org/0000-0002-2640-1502
                Article
                10.1101/2023.11.06.565717
                10659311
                37986846
                a1b393f3-f89d-47f1-963e-e23219a47496

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                This project was supported by grants from the National Institutes of Health #1R01HD090050 and #5R01HD039963 to BC; and #K99HD103778 and #R00HD103778 to JM. DNA was funded by the Duke School of Medicine.
                Categories
                Article

                rete ovarii,ovary,single-cell transcriptomics,single-nucleus transcriptomics,rna sequencing

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